Does an Individual’s Myers-Briggs Type Indicator Preference
Influence Task-Oriented Technology Use?
Pamela Ludford and Loren Terveen
University of Minnesota, Department of Computer Science and Engineering
4-192 EE/CS Building, 200 Union Street SE, Minneapolis, MN 55455, USA
{ludford | terveen}@cs.umn.edu
Abstract: Technology innovators face the challenge of finding representative groups of users to participate in design
activities. In some cases, software applications will target an audience of millions, and the characteristics of the vast
number of potential users are unclear to the design team. In other cases, a technology is so new that the target market of
potential users is not known. The Myers-Briggs Type Indicator (MBTI) measures individual personality preferences on
four dimensions and is used by psychologists to explain certain differences in human behavior. The definitions of the
MBTI dimensions suggest they could be a factor explaining why individuals take different approaches to using software
applications. This study explores whether MBTI preferences affect behavior when individuals perform tasks using three
different software applications. We find a person’s MBTI type influences how they organize email and the informational
features they rely on when using a decision support system.
Keywords: Participatory design, Myers-Briggs Type Indicator, MBTI, persona, personality, collaborative filtering,
recommender system.
1 Introduction
An outstanding challenge in Human Computer Interaction
(HCI) practice is to understand differences in how
individuals use technology. This is necessary since
successful technology development requires input from a
representative set of potential users. The set of potential
users should span the range of differences among
individuals that may influence technology. For various
applications, these factors may include age, gender, job
function, language, culture, etc. In this work, we explore
the use of another factor, the MBTI personality preference.
We investigate the extent to which MBTI personality
dynamics affect technology use and propose it should be
considered when selecting users for design activities.
1.1 The MBTI personality dimensions
The MBTI, based on the theories of psychologist Carl Jung,
measures an individual’s personality preferences over four
dimensions, and is often used by psychologists in career
counseling and group dynamics analysis. The four
dimensions are outlined as follows.
(1) Extraversion /Introversion
A person’s Extravert1/Introvert preference indicates how
he/she gathers energy. Extraverts find themselves energized
by people and activities in the world external to themselves.
Conversely, Introverts gather energy from their own
internal world of thoughts, ideas, and viewpoints.
(2) Sensing /Intuition
A Sensor notices and attends to details. Sensors respond
best to facts, actualities, and react to exactly what was said
rather than implication. On the other hand, Intuitors are big
picture people. They notice patterns, like to make sense of
complexity, and read between the lines.
(3) Thinking/Feeling
The Thinking/Feeling characteristic describes information
individuals’ use in decision-making. The Thinker uses logic
to reach decisions, while the Feeler considers values,
beliefs, and how actions affect other people when making
decisions. The Thinker tends to be objective, whereas the
Feeler is more likely to have a subjective bias.
1
Preferred spelling when referring to the Myers-Briggs Type
Indicator.
(4) Judging /Perceiving
The Judging/Perceiving dimension describes how people
organize their lives. Judgers manage their time by defining
schedules and using “to-do” lists. Being on time is
important to the Judger, and they prefer to make decisions
quickly in order to achieve closure. The Perceiver prefers
spontaneity, likes to leave their options open, and tends to
be less affected when faced with unexpected events.
The labels used to describe the Myers-Briggs
personality dimensions differ from their traditional
meanings in the English language. For example, an MBTI
‘Extravert’ is not necessarily talkative, an MBTI ‘Feeler’ is
not necessarily emotional, and an MBTI ‘Judger’ is not
necessarily judgmental. When using the MBTI as an
instrument for developing technology, it is important to
understand the characteristics measured by each dimension
so the tool can be applied effectively.
1.2 The MBTI as a psychological instrument
A body of empirical research establishes the MBTI’s
soundness as a psychological measure. The MBTI has an
85% reliability rate, where reliability measures the ability
of the instrument to reproduce similar results on subsequent
applications (Briggs Myers et al, 1998). The validity of the
MBTI, the degree to which the instrument measures what it
intends to measure, is estimated at over 75% based on
feedback from psychologists who administer the test and
interpret results (Briggs Myers et al, 1998). The reliability
and validity of the MBTI are considered very good among
psychological instruments available today.
Psychologists who administer the MBTI are careful to
warn there are situations when it is inappropriate to use the
MBTI to explain or predict behavior. For example, MBTI
preference cannot predict whether an individual will be a
good manager (Neuman 2003)2.
Next, the MBTI is suited for use in technology design
because it describes personality traits that transcend culture.
Societal differences are important when designing
technologies for international use, an increasingly common
design scenario (Dray, 2002; Russo, 1993). When Carl
Jung first described psychological types that make up the
MBTI preferences, he believed he depicted preferences
common to the human race. Research shows the MBTI
preferences are exhibited in all cultures (McCalley, 1993)
although the prevalence of MBTI type can vary depending
on the culture (McCaulley, 2002). As of the year 2000, the
MBTI was available in 30 languages (McCalley, 2002).
Therefore, if MBTI proves to be a factor in explaining
2
Personal Communication with Dr. Jody Neuman, Outreach &
Consultation Program Director at University of Minnesota
Counseling & Consulting Services. Dr. Neuman also
confirmed that our use of the MBTI in this study was
appropriate.
different technology use patterns, its use in design will not
be limited to one culture.
The preferences measured by the MBTI, its acceptance
in the psychological community as an effective instrument,
and the assertion that MBTI preferences exist in all cultures
provide motivation to explore whether MBTI type affects
how individuals use computers. This study explores
whether the MBTI affects computer use by targeting how
people use three different software applications to perform
a varied set of tasks.
2 Previous work
2.1 Applying psychological measures
To date, most studies of Human Computer Interaction
and personality type have focused on broad uses of
technology, rather than relating personality type to
more specific, task-oriented technology use. For
example, prior studies have examined how MBTI
preferences affect an individual’s propensity to adopt
computers in day-to-day living and work environments
(Pocius, 1993). The studies look at attitude, aptitude,
and inclination to use computers in a general sense, and
do not address MBTI preference with reference to
specific software applications or ways of using them.
Other work has focused on MBTI type and
performance in computer based learning and testing
environments (Chu and Spires, 1991). The Homenet
project examines psychological factors affecting
computer use, but does not use the MBTI as a study
instrument (Kraut et al., 1998). In addition, several
research studies quantify MBTI preferences common to
Information Technology professionals (Weldon, 1995;
Pocius, 1993) but do not present implications for HCI
design.
A body of three studies (Isbister and Nass 2000;
Nass and Lee 2000; Nass and Lee 2001) examine
whether a person’s introvert/extrovert3 style influences
the computer interface they prefer in various
circumstances. Subjects interact with computeranimated characters and text-to-speech (TTS)
interfaces that mimic either introvert or extrovert
human behavior. The objective is to determine whether
subjects like cartoons and voices that act/sound similar
to themselves. The studies combine use of the MBTI
and the Wiggins personality tests, relying mostly on
constructs from the Wiggins instrument, which differs
significantly from the MBTI.
One study with a narrower scope examines
correlation of email message content and MBTI
preference (Bail, 1995). In this study, a university
professor notices patterns in course-related email
content from students preferring Introversion or
Judging. The researcher later disqualifies the study
3
As spelled in the study literature.
results because the participant student group contains a
large number of non-native English speakers, and he
believes the observed behavioral differences might be
affected by native language instead of, or in addition
to, MBTI preference.
Another body of research examines how personality
traits and MBTI preference affect team dynamics
(Barry and Sewar, 1997; Huszczo, 1996). The studies
show how individual personality styles influence the
ease and effectiveness with which a group
accomplishes assigned tasks. However, neither study
examines this effect in relation to technology use.
2.2 Modeling diverse user populations
In order to characterize users of software that will be massmarketed to large, international audiences, recent work
describes use of Personas, which are commonly used in
marketing to typify product users (Grudin and Pruitt, 2002).
A Persona is a detailed portrayal of a fictional person who
developers believe is, or will be a product user. In
technology development, design teams build use cases
around Personas, helping the team to visualize user needs
during development. Second, a related study found
managers, administrative assistants, and individual
contributors had different work activity patterns that caused
them to value different sets of features when using
electronic calendars (Grudin, 2003). The work showed
organization status and role affected use of a single
software application, and concluded that design should
encompass users from different levels of the workplace.
Our research also attempts to better understand diverse
software users. Rather than using conceptual Personas or
job responsibility to predict differences in how individuals
approach using technology applications, it uses MBTI
preferences. Obviously, MBTI preference will not be the
only factor differentiating technology use; our research
investigates whether it might be one factor. Our study
differs from others examining Human Computer Interaction
and MBTI preference in three ways. First, it looks at
individuals’ use of three specific computer applications: (1)
email, (2) Amazon.com, and (3) the Movielens
personalized movie recommender system. Second, it
focuses on common tasks people perform using the
applications, rather than on general computer use. Finally,
our work differs from the extrovert/introvert studies of
Isbister, Nass, and Lee mentioned earlier in that we use the
MBTI instrument exclusively, rather than combine it
with another psychological instrument.
3 Methodology
Twenty subjects participated in three research exercises,
with participation time ranging from 1 ½ - 3 hours per
subject. Our plan was to determine their individual MBTI
preferences, analyze their use of three software
applications, and look for patterns of use that correlated
with MBTI preference. Our research methods included a
combination of ethnographic, interview and controlled
study techniques. We considered studying a variety of
software applications, choosing three where we believed
differences in MBTI preferences would be likely to result
in different user behaviors.
Task 1: Using the work of Whittaker and Sidner (1996) as
a base, we asked subjects to describe and demonstrate their
email management habits, focusing on job-related email.
The studies took place in subjects’ offices or other
locations where they had access to their work email.
Participants were encouraged to reference their email and
show examples of their organization habits. Participants
provided quantitative information about their email
messages, including the number of email messages
presently in their Inbox, Sent, Deleted Item, and Task lists.
Subjects also supplied the number of folders they had
created and the number of items in selected folders.
Task 2: Participants evaluated at least two books and one
music compact disc (CD) of their own choice on
Amazon.com. Participants were told the goal was to use
information available to decide whether the book or music
CD held personal interest, rather than to decide if they
wanted to make a purchase on Amazon.com. In some cases,
Amazon provided little information about a participant’s
book or music selection; when this happened, subjects were
asked to find a different book or music CD to review.
Task 3: Recommender systems provide product and
service suggestions tailored to individual users. Subjects
used a movie recommender system, Movielens, which
employs collaborative filtering to provide personalized
movie recommendations. After receiving a list of suggested
films from Movielens, participants discussed their reactions
to the film recommendations and to the Movielens web site
content.
Subjects performed all three tasks and took the MBTI
Form M test. The order in which the subjects performed the
exercises was varied across conditions to avoid bias. Scores
from the MBTI test were made available to the researcher
and subject when the study exercises were complete.
The 20 study participants consisted of 13 information
technology professionals, 1 first level manager, 1 financial
planner, 1 elementary school teacher, 1 public relations
consultant, 2 airline schedule planners, and 1 mechanical
engineer. Sixteen of the participants were employees of a
major airline. All participants considered email an
important workplace communication tool, received at least
5 email messages a day, used Microsoft Outlook as their
email manager, and used the Internet at least 2 hours a
week. The MBTI preferences of the participant group were
relatively balanced, as illustrated in Table 1.
Extravert
8
Introvert
12
Sensing
8
Intuitive
12
Thinking
11
Feeling
9
Perceiving
10
Judging
10
Table 1: Participant group MBTI preferences
___________________________________________________
3.1 Hypotheses
The MBTI preference definitions suggest differences in use
of certain major software applications. First, the MBTI
Judging/Perceiving distinction describes differences in
individual organization style. The first study hypothesis
recognizes that email volume causes users to develop
techniques such as using email folders to organize their
correspondence. We postulate that subject’s email
management
habits
will
reflect
their
MBTI
Judging/Perceiving preference.
Second, the MBTI Thinking/Feeling distinction
describes what type of information people use in decisionmaking. With Amazon.com serving as an information
portal in the study, the second study hypothesis proposes
that the information subjects use to assess books and
recorded music on Amazon.com will differ based on their
MBTI Thinking/Feeling preference.
Third, the personalized film recommendations
provided to Movielens users triggers an implicit decisionmaking process for the user: “Should I act on the
recommendation or not?”. Swearingen and Sinha (2001)
found recommender system users value availability of
extra information related to recommendations, as it helps
them determine whether the recommendations make sense.
With this in mind, subjects would use information
available on Movielens and a related site, the Internet
Movie Database, to decide whether they would act on film
recommendations. They would then be asked if there were
additional features Movielens could provide to help in their
decision. The third study hypothesis proposes that
suggestions for additional Movielens features reflect the
subject’s MBTI Thinking/Feeling preference.
_______________________________________________
MBTI dimension
Task
Judging/Perceiving
Thinking/Feeling
Thinking/Feeling
organization
decision-making
decision-making
Technology
Application
email
Amazon.com
Movielens
Table 2: Summary of exercises and hypotheses
Post study analysis took a broader view, and examined
whether there were relationships between application use
and MBTI preferences that were not reflected in the three
hypotheses. No unexpected correlations were found.
4 Observations
4.1 Email management
When describing their habits for email management,
subjects discussed and demonstrated the norms they had
developed for handling different types of email, as well as
practices they had adopted for periodically cleaning their
Inbox and email archives.
The study addressed how participants handled taskrelated and informational mail, mail of indeterminate status
(Whittaker and Sidner, 1996), and social invitations.
First, task-related email contains either an implicit or
explicit request for the recipient to perform a work-related
task. It includes threads of email sharing the same subject
line where recipients exchange information about the task
and related issues. Second, informational email
encompasses work-related mail that is purely informational
and does not require the recipient to take action upon
receipt. Third, mail of indeterminate status includes mail
that has been read, but where the recipient has not
determined what action should be taken as a result. For
example, Mary receives an email asking her to run a report
for the Business Expense project, but her co-worker Bob is
assigned to the project. She decides she will check with
Bob before handling the email. Prior to checking with him,
the mail is in indeterminate status. Finally, social invitation
email includes correspondence that invites the recipient to
attend a social event.
Participants described occasions when they had
received each of the four types of email, explaining what
typically happened to each type of mail after it had been
received and read. The study showed that handling of
informational mail, mail of indeterminate status, and social
invitation mail was unaffected by MBTI preference. Taskrelated email management, however, correlated with the
subject’s MBTI Judging/Perceiving preference, supporting
our first hypothesis.
All study participants, regardless of MBTI preference,
kept task-related email in their Inbox while the task was in
progress. When the related task was complete, however, the
majority of participants who were MBTI ‘Judgers’ reported
a preference for either deleting the email, or moving it to a
folder for reference. In contrast, most MBTI ‘Perceivers’
preferred to keep email related to completed tasks in the
Inbox for lengths of time varying from several days to more
than a year. ‘Perceivers’ had varied rationales for keeping
completed tasks in the Inbox.
“I have to report how much time I spent on projects I’m
working on every week, and I can remember what I worked
on by looking at what was in my Inbox that week.”
“The server that my Inbox is on is backed up every
night, so if there’s a crash I won’t lose my email.”
“I need a computer with lots of space. I keep almost
everything in my Inbox forever. If I need to find the email
address of somebody who sent me mail last June, I sort my
Inbox by date and find their email from last June, and there
it is.”
The difference between Judgers and Perceivers
handling of task-related emails is significant with p = (.01)
using a Fisher Exact test. Results are shown in Figure 1.
During the email management study exercises,
participants provided an implicit description of how they
used their Inbox as a task manager and/or reference point
(Whittaker and Sidner, 1996). For various reasons, all study
participants considered Inbox space to be valuable
electronic real estate.
“I know it’s time to clean my Inbox when I have to
scroll down to see everything in it. I use my Inbox as a
reminder of things I have to do, and if I can’t see
everything, I might forget to do something important.”
“I like to keep as much as possible in my Inbox because
it is easier to sort and find things there … it’s all in one
place.”
The above examples illustrate two uses of the Inbox,
first, as a task manager and second, as a reference point
providing easy retrieval. ‘Perceivers’ were apt to use the
Inbox as a storage point for completed task-related email,
while ‘Judgers’ did not exhibit the same behavior, and
restricted use of the Inbox to that of a current task manager.
It seems logical that MBTI preference could predict
habits for handling email. Because ‘Judgers’ like finishing
tasks to achieve closure, deleting task-related email or
moving it to a folder upon task completion seems symbolic
of their preference for closure. In contrast, ‘Perceivers’ like
to keep their options open, and may regard keeping
completed tasks in the Inbox upon completion as analogous
to leaving open the option to find and refer to them again.
A valid question would be whether ‘Perceivers’ used
folders to archive email. According to quantitative data
collected, there was no difference between the amount of
folders ‘Judgers’ and ‘Perceivers’ had created, or the
amount of email in the folders. The difference was
‘Perceivers’ delayed moving task-related emails from the
Inbox to folders longer than ‘Judgers’ did.
A factor that could influence how people handle
incoming email is the number of messages their Inboxes.
To test whether this affected our results, we recorded the
number of messages in each subject’s Inbox at the time the
experiment was conducted, and found no significant
difference in ‘Judgers’ and ‘Perceivers’ Inbox sizes (t-test,
p = .15, df = 18, t = 1.50). So, number of mails in the Inbox
did not have a bearing on email management behavior. The
average number of emails a person receives could also
affect how they manage their email, and a future study
could address whether this factor is influential.
We found behavior differences between ‘Perceivers’
and ‘Judgers’ exist within a single job description. In
studying email management, we observed two study
subjects, both Data Warehouse Analysts at the same
company, one an MBTI ‘Perceiver’ and the other an MBTI
‘Judger’, had very different habits for organizing their
email. As summarized earlier, the ‘Perceiver’ preferred
using her Inbox as a long-term storage device for
completed task email, while the ‘Judger’ was careful to
move completed task mail out of the Inbox on completion.
This result suggests that characterizing users based soley on
job function is not sufficient, and further research
incorporating this finding with other user characterization
models is clearly needed.
10
9
8
7
Number of 6
5
Subjects
4
3
2
1
0
Perceiver
Judger
Completed Task Email:
Kept in
Deleted or
Inbox
Moved to
Folder
Figure 1: Task-related email handling differences. Results
significant with p = (.01), using Fisher Exact test
_______________________________________________
4.2 Amazon.com as a decision support vehicle
The Amazon.com web site merchandises a variety of
products, including books and recorded music, and
provides a wide variety of information about individual
products offered for sale. In addition to price, customers
evaluating books on Amazon can view excerpts from the
book, read editorial and customer reviews, and view
recommendations for books. Amazon provides similar
information about music CDs, with track listings and audio
samples also available.
In this part of the study, participants evaluated at least
two books and one music CD of their own choice on the
Amazon.com web site. When selecting products to
evaluate, subjects were asked to avoid viewing products
they already owned or had already decided they wanted to
acquire. A goal of this part of the study was to compare
information used in product evaluation by MBTI type.
Subjects examined information available on Amazon,
speaking out loud about information they noticed and used
in their assessment.
Varying amounts of information is available for
Amazon merchandise. For example, the ‘Look Inside’
feature that allows one to view excerpts from a book is
available for many, but not all Amazon.com book
selections. To account for differences in available
information for individual books and music CDs, subjects
looked at books and CDs that contained at least the
following features.
________________________________________________
Books
Price
Look Inside
Available Used/New
Customers who bought
this book also bought
(book recommendations)
Customer Rating
Editorial Reviews
Customer Reviews
Music
Price
Track Listings
Audio Music Samples
Customers who bought
this CD also bought
(music recommendations)
Customer Rating
Editorial Reviews
Customer Reviews
Table 3: Minimum set of Amazon.com features available for
evaluated items
________________________________________________
Data evaluation included looking for trends between
MBTI preference and the criteria subjects used to evaluate
books and music CDs. As hypothesized, the correlations
discovered related to the individual’s Thinking/Feeling
preference.
Books: Subjects with an MBTI ‘Feeling’ preference were
more likely to read and use Customer Review content when
assessing their interest in a book than MBTI ‘Thinkers’
were. The results were significant using a Chi square test
with 1 degree of freedom, with p = (.02), and are shown in
Table 4. Some of the subjects using Customer Reviews in
product assessment explained they read the review, made a
determination if they shared a common viewpoint with the
reviewer, and when they did, they considered the contents
of the review in their evaluation. In contrast, MBTI test
subjects preferring ‘Thinking’, made comments about
Customer Reviews that reflected their preference for logic
and objectivity, as illustrated below.
“I bet these reviews are only from people who liked the
book. If they’re trying to sell books, why would they put a
bad review out here?”
“It looks like people can write anything that want in
these. I wouldn’t use that.”
“Who is this person? Why should I listen to them?”
Participants preferring ‘Thinking’ did, however, use
Editorial Reviews when assessing books, so ‘Thinkers’
differentiated between available reviews based on the
authority of the reviewer.
Study participants who used Amazon Customer
Reviews in book evaluation volunteered suggestions for
improving them. For example, two study participants noted
that Customer Reviews were unstructured, free-form text,
and explained it would be useful to them if the contents
were categorized. Additionally, participants explained there
was risk in reading Customer Reviews because there was a
possibility the reviewer might give away too much of the
plot. Both comments provide implications for interface and
content design, and illustrate different technology needs
reflecting MBTI preference.
The outcome suggests MBTI ‘Feelers’, interested in
the views of other Amazon customers, might have a general
interest in collaboration interfaces and/or online
communities as a resource in decision-making, and is an
avenue for further exploration.
Music: When it came to recorded music evaluation, those
with an MBTI ‘Thinking’ preference were more likely to
read and use Editorial Review content than MBTI
‘Feelers’. The results were significant using a Chi square
test with 1 degree of freedom, with p = (.02), and are
shown in Table 4. Many ‘Feelers’ explained they
employed different criteria to evaluate books and music.
They said they acquired music CDs because they had heard
a song or two from a CD, liked what they heard, and
bought the CD based on that. Many volunteered they rarely
considered Editorial or Customer Reviews when it came to
choosing music CDs.
The results of the Amazon.com music evaluation
exercise seem plausible: people with an MBTI ‘Feeling’
preference are more likely to make decisions based on
instinct. When they choose music based on hearing it and
liking it, it follows that their decision is based on their
instinctive sense about the music. On the other hand, the
MBTI ‘Thinker’ subjects in the study sought to confirm
their partiality for the music with a professional critic’s
review before making a decision.
________________________________________________
Books
Music
Used Customer Reviews
Thinking
Feeling
3/11*
7/9*
3/11
2/9
Used Editorial Reviews
Thinking Feeling
9/11
7/9
8/11*
2/9*
Table 4: Selected results from Amazon.com task. Significant
differences are noted by *.
For each result, significance was determined using a Chi square
test, 1 degree of freedom, with p = (.02).
________________________________________________
One interesting and unexpected result of the Amazon
task was that individuals used distinctly different
information to evaluate books and music online. Because
both entities often serve as personal, leisure, or
entertainment vehicles, the prediction might be that
individuals use similar information to assess them. As it
turns out, the evaluation criteria were unique to the product,
and reflected MBTI preference. Previous research
(Swearingen and Sinha 2001) shows users value and rely
on supporting information about a product provided by a
recommender system. Additional research is needed to
understand which types of information are most valuable
for particular content domains, user tasks, and user
personality types. The difference between the book and
movie domain found in our study is one specific topic that
deserves further investigation. Results of additional
research would allow recommender system designers to
improve their systems and/or target them to particular
users.
Finally, because the Internet and other electronic
information portals have proven to be valuable information
resources for decision-makers (Sellen et al., 2002), and
because the MBTI Thinking/Feeling preference describes
information differing individuals use in decision-making,
future work could compare features individuals use and
prefer in other types of decision support systems.
4.3 MovieLens observations
Recommender systems provide personalized product and
service suggestions. The Movielens application (Herlocker,
et al., 2000) recommends films to individuals by using
collaborative filtering to match an individual’s historical
movie preferences with other application users who have
expressed similar tastes. Movielens users rate films they
have viewed in the past, and based on their ratings,
Movielens recommends other movies of potential interest
to the individual.
Study participants, none of whom had used Movielens
prior to the exercise, began by rating 10 films they had seen
in the past. Movielens then provided fifteen personalized
film recommendations to the subject. Participants discussed
their reactions to the recommendations, and also shared
their views about the Movielens web site content.
Most subjects did not recognize several of the suggested
movies, so participants chose two unfamiliar films, and
viewed information about them on Movielens and/or the
www.imdb.com web site. The IMDB (Internet Movie Data
Base) provides plot summaries, cast listings, professional
critic reviews, user reviews, and other movie information.
Participants explained what information was useful in
evaluating the movie, and also explained what (if any)
further information could have been provided to help them
decide whether or not they would act on the
recommendation.
The suggestions received did not correlate to participant
MBTI preference, and the study’s third hypothesis was not
supported. A wide variety of ideas were verbalized,
including expansion of plot summaries, adding genre to the
list of recommended films, direct links from Movielens to
professional critic reviewers websites or local theater show
times, and many others. Perhaps the open ended nature of
the question, the large universe of possible answers, and the
limited amount of time participants had to consider their
response resulted in what appeared to be a long list of good
suggestions, but without pattern based on MBTI
preference.
Exploration of the same topic using a different
methodology might provide different results. For example,
a future study might examine if participants who use
Movielens over an extended period of time make different
suggestions for improvements to the web site than those
found in this study. Results could be cross-checked with
MBTI preference in order to determine if new feature
suggestions follow MBTI preference. Further study could
also address whether MBTI preference affects the
likelihood that users follow recommender system
suggestions.
Conclusion
There is little similar work examining how MBTI
preferences influence task-oriented technology use.
Therefore, our study contributes to HCI practice both by
raising issues for inquiry and by presenting some initial
results
First, we found that two people with the same job
function may use applications differently, and the
differences, as observed in this study, can be explained by
MBTI preference. Designing with the assumption that
people with the same job function use software in a similar
fashion will fail to provide a complete picture. Additional
work could explore how MBTI preference could be
combined with other types of user characterizations, such
as Persona or activity patterns, to produce a rich blend of
typical user traits for application in technology design.
Empirical research proving MBTI preference affects
how people use technology might help justify the cost of
participatory design. While proponents of participatory
design understand the benefits of focusing technology
development on user practices, it can be difficult to
convince those inexperienced with the method it is
necessary to include a varied set of users in the process.
Research results can establish individuals have different
styles for using technology, can illustrate how the
differences are rooted in individual personality, and can
show broad inclusion of users in participatory design has
merit.
Research has shown MBTI preferences are exhibited in
all cultures. If future technology design were to include
features satisfying users with varied MBTI preferences, the
prediction is that applications would translate well across
cultures. With much at stake in release of major
technology, future work could confirm if this prediction is
true.
We believe more research is needed to understand how
all four MBTI dimensions predict technology use. With
little or no empirical work directed at the first two MBTI
dimensions, Extravert/Introvert and Sensing/Intuition, these
areas invite further study. For example, the
Extravert/Introvert personality dimension suggests the
possibility of behavioral differences in electronic
communication
and collaboration. Because the
Judging/Perceiving dimension describes how individuals
organize their lives, technology applications such as
electronic calendars and Personal Digital Assistants may be
improved by considering MBTI differences.
In conclusion, we have shown some cases where
software use reflected MBTI personality preference, and
have uncovered patterns of use not identified by other
methods. As a result, we have illustrated that choosing
users with diverse MBTI personality characteristics can
enrich HCI design practice.
Acknowledgements
Special thanks to the study participants who so willingly
volunteered their time for the project. Thanks also to Jody
Neuman who shared her MBTI expertise, and Jamelyn
Johnson at the CAPT library. Finally, we express our
appreciation to Jon Nelson and Dan Cosley, who provided
valuable editorial comments.
References
Bail, F.T. (1995). An Exploration of Relationships Among
Psychological Type, Ethnicity and Computer-Mediated
Communication. In R.A. Moody (Ed.), Psychological Type
and Culture- East and West: A Multicultural Research
Symposium (p. 221-228).
Herlocker, J., Konstan, J., and Riedl, J. (2000). Explaining
Collaborative Filtering Recommendations. Proceedings of
the ACM 2000 Conference on Computer Supported
Cooperative Work.
Isbister, K., Nass, C. (2000) Consistency of personality in
interactive characters: Verbal cues, non-verbal cues, and
user characteristics. International Journal of HumanComputer Studies, 53 (2), p. 251-267.
Kraut, R., Mukhopadhyay, T., Szczypula, J., Kiesler, S.,
Scherlis, W., (1998). Communication and Information:
Alternative Uses of the Internet in Households. Proc. CHI
1998, Extended Abstracts, p. 368-375.
McCaulley, M. H. (1993, October). Does a country or a
culture have a type? Gainesville, FL: Center for
Applications of Psychological Type.
http://www.capt.org/Research/Home.cfm
McCaulley, M. (2002). Consider How Far We’ve Come in 25
Years. Bulletin of Psychological Type 23:7, Fall 2002 p. 2022.
Nass, C., Lee, K.M. (2000) Does computer-generated speech
manifest personality? An experimental test of similarityattraction. Proc. CHI 2000, p. 329-336.
Barry, B. and Stewar, G.L. (1997). Composition, Process, and
Performance in Self-Managed Groups: The Role of
Personality. Journal of Applied Psychology, 82, 62-78.
Nass, C., Lee, K.M. (2001) Does computer-synthesized speech
manifest personality? Experimental test of recognition,
similarity-attraction, and consistency-attraction. Journal of
Experimantal Psychology: Applied, 7 (3), p. 171-181.
Briggs Myers, I., McCaulley, M., Quenk, N., Hammer, A.
(1998). MBTI Manual (A), A Guide to the Development
and Use of the Myers-Briggs Type Indicator. Consulting
Psychologists Press, Inc.
Pocius, K. (1991). Personality Factors in Human-Computer
Interaction: A Review of the Literature. Computers in
Human Behavior, 7(3), p. 103-135.
http://www.capt.org/Research/Home.cfm
Chu, P. C. & Spires, E. E. (1991). Validating the Computer
Anxiety Rating Scale: Effects of cognitive style and
computer courses on computer anxiety. Computers in
Human Behavior, 7(1-2), 7-21.
Swearingen, K., Sinha, R. (2001). Beyond Algorithms: An HCI
Perspective on Recommender Systems. In SIGIR workshop
on Recommender Systems.
Dray, Susan (1996). Designing for the Rest of the World: A
Consultants Observation. CHI Interactions, March 1996. p.
15-18.
Grudin, J., Pruitt, J. (2002) Personas, Pariticipatory Design, and
Product Development: An Infrastructure for Engagement.
Proc. Participatory Design Conference 2002, p.144-161.
Grudin, J. (2003). Managerial Use and Emerging Norms:
Effects of Activity Patterns on Software Design and
Deployment.
http://www.research.microsoft.com/research/coet/Roles/TRs
/01-91.pdf
Huszczo, G. (1996). Tools for Team Excellence: Getting Your
Team into High Gear & Keeping It There. (Eds.) DaviesBlack Publishing, Palo Alto, CA, USA.
Russo, P., Boor, S. (1993). How Fluent is Your Interface?
Designing for International Users. InterCHI, 1993 p. 342347
Sellen, A., Murphy, R., Shaw, K. (2002). How Knowledge
Workers Use the Web. Proc. CHI 2002. Extended Abstracts,
p. 227-234.
Weldon, D. (1995). A Mutual Understanding. Computerworld,
May 1, 1995. p. 103-111.
Whittaker, S. and Sidner, C. (1996). Email overload: exploring
personal information management of email. Proc. CHI
1996. Extended Abstracts p. 276-283.